US11726461B2 - Method, apparatus, electronic device, medium, and program product for monitoring status of production order - Google Patents

Method, apparatus, electronic device, medium, and program product for monitoring status of production order Download PDF

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US11726461B2
US11726461B2 US17/763,563 US201917763563A US11726461B2 US 11726461 B2 US11726461 B2 US 11726461B2 US 201917763563 A US201917763563 A US 201917763563A US 11726461 B2 US11726461 B2 US 11726461B2
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product
production
data
model
iot
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US20220350315A1 (en
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Ming Yu
Liang Zhang
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Siemens AG
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing

Definitions

  • the disclosure generally relates to the technical field of the Internet of things (IoT).
  • IoT Internet of things
  • Various embodiments of the teachings herein include methods, apparatuses, electronic devices, media, and program products for monitoring the status of a production order.
  • AI artificial intelligence
  • other intelligence technologies can help process more and more data so that the system can run more efficiently.
  • AI artificial intelligence
  • a large number of factories have some legacy devices only with a limited number of data interfaces and cannot provide abundant information for product process monitoring and optimization.
  • some embodiments include a method for monitoring the status of a production order in a factory, comprising: generating at least one production IoT model on the basis of a production scheduling system document, the production IoT model comprising at least process attributes of product processing, generating at least one product IoT model on the basis of a product design specification document, the product IoT model also comprising at least process attributes of product processing, associating a production IoT model with a product IoT model having the same process attributes, learning data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product, and matching the processing steps against the process attributes of the product IoT model and determining the status of the production order in the factory on the basis of the matching result.
  • generating at least one production IoT model on the basis of the production scheduling system document comprises generating a production IoT model for each order No. in the production scheduling system document.
  • generating at least one product IoT model on the basis of a product design specification document comprises extracting product metadata from a software design tool to generate the product IoT model.
  • matching the processing steps against the process attributes of the product IoT model and determining the status of the production order in the factory on the basis of the matching result comprises determining the product and the order No. the current device processes according to the production IoT model and the product IoT model if a data change of the processing steps in the data model matches the process attributes of the product IoT model.
  • the data acquisition automation control system comprises at least one of a vibration sensor, a current sensor, a temperature sensor and a humidity sensor.
  • learning data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product comprises using a data clustering engine to learn the data to obtain a data model representing processing steps of a product on the basis of at least one of the change time of data, the change period of data and the amplitude of data.
  • some embodiments include an apparatus ( 200 ) for monitoring the status of a production order in a factory, comprising: a production IoT model generation unit ( 202 ), configured to generate at least one production IoT model on the basis of a production scheduling system document, the production IoT model comprising at least process attributes of product processing, a product IoT model generation unit ( 204 ), configured to generate at least one product IoT model on the basis of a product design specification document, the product IoT model also comprising at least process attributes of product processing, an IoT model association unit ( 206 ), configured to associate a production IoT model with a product IoT model having the same process attributes, a data model acquisition unit ( 208 ), configured to learn data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product, and an order status determination unit ( 210 ), configured to match the processing steps against the process attributes of the product IoT model and determine the status of the production order in
  • the production IoT model generation unit ( 204 ) is further configured to generate a production IoT model for each order No. in the production scheduling system document.
  • the product IoT model generation unit ( 206 ) is further configured to extract product metadata from a software design tool to generate the product IoT model.
  • the order status determination unit ( 210 ) is further configured to determine the product and the order No. the current device processes according to the production IoT model and the product IoT model if a data change of the processing steps in the data model matches the process attributes of the product IoT model.
  • the data acquisition automation control system comprises at least one of a vibration sensor, a current sensor, a temperature sensor and a humidity sensor.
  • the data model acquisition unit ( 208 ) is further configured to use a data clustering engine to learn the data to obtain a data model representing processing steps of a product on the basis of at least one of the change time of data, the change period of data and the amplitude of data.
  • some embodiments include an electronic device ( 800 ), comprising: at least one processor ( 802 ), and a memory ( 804 ) coupled with the at least one processor ( 802 ), the memory being configured to store instructions, and when the instructions are executed by the at least one processor ( 802 ), the processor ( 802 ) executing a method as described herein.
  • some embodiments include a non-transient machine-readable storage medium, storing executable instructions, and when the instructions are executed, the machine executing a method as described herein.
  • some embodiments include a computer program, comprising computer-executable instructions, and when the computer-executable instructions are executed, at least one processor executing a method as described herein.
  • some embodiments include a computer program product, the computer program product being tangibly stored in a computer-readable medium and comprising computer-executable instructions, and at least one processor executing a method as described herein.
  • FIG. 1 is an exemplary flowchart of a method for monitoring the status of a production order in a factory incorporating teachings of the present disclosure.
  • FIG. 2 is a block diagram of an exemplary configuration of an apparatus for monitoring the status of a production order in a factory incorporating teachings of the present disclosure.
  • FIG. 3 is a schematic diagram of a production IoT model incorporating teachings of the present disclosure.
  • FIG. 4 is a schematic diagram of a product IoT model incorporating teachings of the present disclosure.
  • FIG. 5 is a schematic diagram of a vibration data model incorporating teachings of the present disclosure.
  • FIG. 6 is a schematic diagram of the match between a product IoT model and a data model incorporating teachings of the present disclosure.
  • FIG. 7 is a schematic diagram of an incorrect vibration data model incorporating teachings of the present disclosure.
  • FIG. 8 is a block diagram of the electronic device for monitoring the status of a production order incorporating teachings of the present disclosure.
  • a method for monitoring the status of a production order in a factory comprises: generating at least one production IoT model on the basis of a production scheduling system document, the production IoT model comprising at least process attributes of product processing; generating at least one product IoT model on the basis of a product design specification document, the product IoT model also comprising at least process attributes of product processing; associating a production IoT model with a product IoT model having the same process attributes; learning data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product; and matching the processing steps against the process attributes of the product IoT model and determining the status of the production order in the factory on the basis of the matching result.
  • generating at least one production IoT model on the basis of the production scheduling system document comprises generating a production IoT model for each order No. in the production scheduling system document.
  • generating at least one product IoT model on the basis of a product design specification document comprises extracting product metadata from a software design tool to generate the product IoT model.
  • matching the processing steps against the process attributes of the product IoT model and determining the status of the production order in the factory on the basis of the matching result comprises determining the product and the order No. the current device processes according to the production IoT model and the product IoT model if a data change of the processing steps in the data model matches the process attributes of the product IoT model.
  • the data acquisition automation control system comprises at least one of a vibration sensor, a current sensor, a temperature sensor and a humidity sensor.
  • learning data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product comprises using a data clustering engine to learn the data to obtain a data model representing processing steps of a product on the basis of at least one of the change time of data, the change period of data and the amplitude of data.
  • an apparatus for monitoring the status of a production order in a factory comprises: a production IoT model generation unit, configured to generate at least one production IoT model on the basis of a production scheduling system document, the production IoT model comprising at least process attributes of product processing; a product IoT model generation unit, configured to generate at least one product IoT model on the basis of a product design specification document, the product IoT model also comprising at least process attributes of product processing; an IoT model association unit, configured to associate a production IoT model with a product IoT model having the same process attributes; a data model acquisition unit, configured to learn data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product; and an order status determination unit, configured to match the processing steps against the process attributes of the product IoT model and determine the status of the production order in the factory on the basis of the matching result.
  • a production IoT model generation unit configured to generate at least one production IoT model on
  • the production IoT model generation unit is further configured to generate a production IoT model for each order No. in the production scheduling system document.
  • the product IoT model generation unit is further configured to extract product metadata from a software design tool to generate the product IoT model.
  • the order status determination unit is further configured to determine the product and the order No. the current device processes according to the production IoT model and the product IoT model if a data change of the processing steps in the data model matches the process attributes of the product IoT model.
  • the data acquisition automation control system comprises at least one of a vibration sensor, a current sensor, a temperature sensor and a humidity sensor.
  • the data model acquisition unit ( 208 ) is further configured to use a data clustering engine to learn the data to obtain a data model representing processing steps of a product on the basis of at least one of the change time of data, the change period of data and the amplitude of data.
  • an electronic device comprises at least one processor and a memory coupled with the at least one processor, the memory is configured to store instructions, and when the instructions are executed by the at least one processor, the processor executes one or more of the above-mentioned methods.
  • a non-transient machine-readable storage medium stores executable instructions, and when the instructions are executed, the machine executes one or more of the above-mentioned methods.
  • a computer program comprises computer-executable instructions, and when the computer-executable instructions are executed, at least one processor executes one or more of the above-mentioned methods.
  • a computer program product is tangibly stored in a computer-readable medium and comprises computer-executable instructions, and when the computer-executable instructions are executed, at least one processor executes one or more of the above-mentioned methods.
  • the methods and apparatuses described herein for monitoring the status of a production order can be used to match raw data in factory production against a production procedure to determine the production status in a factory, for example, the status of an order and the status of a device. Thus, the cost of marking data is reduced and the production is more efficient.
  • the method and apparatus can help the manager to learn the production status in the factory and schedule the production.
  • the term “comprise” and its variants are open terms and mean “include but are not limited to.”
  • the term “on the basis of” means “at least partially on the basis of.”
  • the terms “an embodiment” and “one embodiment” mean “at least one embodiment.”
  • the term “another embodiment” means “at least one other embodiment.”
  • the terms “first” and “second” can refer to different or identical objects. Other definitions, explicit or implicit, may be included below. Unless otherwise specified in the context, the definition of a term is consistent throughout the description.
  • the disclosure provides methods of using a production scheduling system document, product design specification documents, and data acquired by a data acquisition automation control system in a factory to automatically label data.
  • a data model representing the status features of data is obtained on the basis of data acquired by the data acquisition automation control system, the determined data model is matched against the IoT models generated on the basis of a production scheduling system document and a product design specification document to supplement context information of data, and the status of a production order, for example, the product and the order number the current device processes, is determined according to the matching result.
  • the cost of marking data is reduced and the production is more efficient.
  • the solution can help the manager to learn the production status in a factory and schedule the production.
  • FIG. 1 is an exemplary flowchart of a method 100 for monitoring the status of a production order in a factory incorporating teachings of the present disclosure.
  • First generate at least one production IoT model on the basis of a production scheduling system document in block S 102 in FIG. 1 .
  • the production IoT model is a model used to represent the production status of a production order. Specifically, generate a production IoT model for each order No. in the production scheduling system document.
  • FIG. 3 is a schematic diagram of a specific example of a production IoT model 300 .
  • the production IoT model 300 shown in FIG. 3 is an IoT model about a production order 301 , for example, 04001180301.
  • the IoT model may comprise an order No. 302 , for example, 04001180301, a start time 303 , for example, April 8 304 , and an end time 305 , for example April 18 306 ;
  • the processes comprised in the order include C14, C03 and C07, wherein 307 represents the next process, that is to say, the next process after C14 is C03, the next process after C03 is C07, and 308 represents the previous process.
  • a production IoT model may be generated according to a supervisory control and data acquisition (SCADA) system description file.
  • SCADA supervisory control and data acquisition
  • a production IoT model may be set manually by an operator.
  • Different production IoT models comprising different object attributes, relationships between attributes and relationships between objects, for example, may be set for different production lines.
  • the product IoT model is a model used to represent production process information of a product.
  • the product IoT model can be used to determine important metadata of a production line to obtain feature information of production.
  • both the generated production IoT model and the product IoT model comprise process attributes of product processing, and the two IoT models can be associated through the process attributes.
  • Product metadata may be extracted from a software design tool, for example, product lifecycle management (PLM) or EPLAN, and the product IoT model is generated on the basis of the metadata.
  • PLM product lifecycle management
  • EPLAN product lifecycle management
  • the product design specification document may be a tabular document, XML document or CVS document, for example.
  • the product design specification document may be a tabular document.
  • the header in each column in the product design specification document can be used as the attribute of the product IoT model and the value in each column can be used as the value of the attribute of the product IoT model to generate the product IoT model.
  • the data acquisition automation control system may include but is not limited to at least one of the following: vibration sensor, current sensor, temperature sensor and humidity sensor. These sensors are utilized to acquire data of the production device.
  • a data clustering engine may be used to learn data on the basis of at least one of three dimensions of data: the change time of data, the change period of data and the amplitude of data, for example.
  • the data clustering engine may mark and analyze the events in sensor data to obtain a data model and the data model may represent the processing steps of a product.
  • the data model can also be obtained by using the statistical method or machine learning method to perform a statistical analysis or machine learning for the data obtained from monitoring.
  • the rules and parameters adopted may be updated according to the feedback from users during the data analysis or machine learning to update the data model.
  • the processing steps may be matched against the process attributes of the product IoT model through the following procedure: First, a search is made in the product IoT model to find whether a variable exists which changes together with the data of processing steps in the data model and has the same period or the same amplitude. If such a variable exists, it is considered that the product IoT model matches the data model, and the product and the order No. the current device processes may be determined according to the attribute values in the production IoT model and the product IoT model.
  • the product IoT model may be matched against the data model obtained on the basis of data acquired by another sensor until the product IoT model is matched against all data models obtained on the basis of data acquired by different sensors. If no matched variable is found, feedback may be given that the necessary data does not exist.
  • the methods for monitoring the status of a production order can be used to match raw data in factory production against a production procedure to determine the production status in a factory, for example, the status of an order and the status of a device.
  • the cost of marking data is reduced and the production is more efficient.
  • the method can help the manager to learn the production status in the factory and schedule the production.
  • FIG. 2 is a block diagram of an exemplary configuration of the apparatus 200 for monitoring the status of a production order in a factory incorporating teachings of the present disclosure.
  • the apparatus 200 for monitoring the status of a production order in a factory comprises a production IoT model generation unit 202 , a product IoT generation unit 204 , an IoT model association unit 206 , a data model acquisition unit 208 and an order status determination unit 210 .
  • the production IoT model generation unit 202 is configured to generate at least one production IoT model on the basis of a production scheduling system document and the production IoT model comprises at least process attributes of product processing.
  • the product IoT model generation unit 204 is configured to generate at least one product IoT model on the basis of a product design specification document and the product IoT model also comprises at least process attributes of product processing.
  • the IoT model association unit 206 is configured to associate a production IoT model with a product IoT model having the same process attributes.
  • the data model acquisition unit 208 is configured to learn data of a production device acquired by a data acquisition automation control system in the factory to obtain a data model representing processing steps of a product.
  • the order status determination unit 210 is configured to match the processing steps against the process attributes of the product IoT model and determine the status of the production order in the factory on the basis of the matching result.
  • the production IoT model generation unit 204 is further configured to generate a production IoT model for each order No. in the production scheduling system document.
  • the product IoT model generation unit 206 is further configured to extract product metadata from a software design tool to generate the product IoT model.
  • the order status determination unit 210 is further configured to determine the product and the order number the current device processes according to the production IoT model and the product IoT model if a data change of the processing steps in the data model matches the process attributes of the product IoT model.
  • the data acquisition automation control system comprises at least one of a vibration sensor, a current sensor, a temperature sensor and a humidity sensor.
  • the data model acquisition unit 208 is further configured to use a data clustering engine to learn the data to obtain a data model representing processing steps of a product on the basis of at least one of the change time of data, the change period of data and the amplitude of data. Details about the operations and functions of the parts of the apparatus 200 for monitoring the status of a production order in a factory may be, for example, the same as or similar to the related parts of the embodiment of the method 100 for monitoring the status of a production order in a factory, described in combination with FIG. 1 , and will not be described here again.
  • the structure of the apparatus 200 for monitoring the status of a production order in a factory and the constitutional units in FIG. 2 is only exemplary and those skilled in the art may modify the block diagram of the structure shown in FIG. 2 as required.
  • a traditional machine tool has no data interface for remote control and status check. Therefore, it is impossible to directly track the status of an order on such a machine tool.
  • the working status of the machine tool may be apparent after sensors, for example, a current sensor, a vibration sensor, a temperature sensor and a humidity sensor, are installed.
  • Table 1 is a production scheduling system document obtained from an order management system.
  • order No. 04001180301 involves three processes C14, C03 and C07.
  • Information such as the workstation at which the machine tool works at a point in time can be learned from the production schedule.
  • the production IoT model 300 shown in FIG. 3 can be obtained for order No. 04001180301.
  • the IoT model 300 shown in FIG. 3 comprises an order No., start time, end time, process Nos. and relationships thereof.
  • Table 2 is an example of a product design specification form.
  • the parameters of the product IoT model 400 shown in FIG. 4 can be obtained.
  • the IoT model in FIG. 4 can be associated with the IoT model in FIG. 3 through the process attribute C03.
  • the object 401 indicates that the unit No. is V100971.
  • the process parameter 402 comprises four processing steps: 4021 , 4022 , 4023 and 4024 .
  • the four process steps comprise processing time 4021 - 1 , 4022 - 1 , 4023 - 1 and 4024 - 1 and rotational speeds 4021 - 2 , 4022 - 2 , 4023 - 2 and 4024 - 2 , respectively, wherein the processing time is 2 minutes.
  • the IoT model in FIG. 4 also shows that the next process 403 after the process attribute C03 is C07 and the previous process 404 is C14. Some attributes which the processes C07 and C14 comprise are omitted in FIG. 4 .
  • the IoT model in FIG. 4 further comprises a tool 405 .
  • the tool 405 may include a machine tool 4051 and the No. of the machine tool 4051 is X6140.
  • the parameters of the product IoT model can also be obtained from the data acquisition automation control system in a factory.
  • recommended values of the parameters can also be obtained from product specifications.
  • the production procedure of the machine tool X6140 for the unit V101971 in order No. 04001180301 may be determined to be as follows:
  • the machine tool performs four processing steps in the process C03.
  • next processes in two orders may be compared until different processes in two orders are found.
  • FIG. 5 is a schematic diagram of a vibration data model 500 .
  • the following production procedure may be determined from the vibration data model shown in FIG. 5 :
  • the machine tool X6140 involves four steps S1, S2, S3 and S4 in the process C03.
  • the production status shown in the production IoT model and the production process information of the product shown in the product IoT model may be compared with the data model obtained on the basis of data learning. Specifically, first, a search is made in the product IoT model to find whether a variable exists which changes together with the data of processing steps in the vibration-data-based data model and has the same period or the same amplitude. If such a variable exists, the product and the order No. the current device processes may be determined according to the product IoT model.
  • the product IoT model may be matched against the data model obtained on the basis of data acquired by another sensor until the product IoT model is matched against all data models obtained on the basis of data acquired by different sensors, for example, current data acquired by a current sensor or temperature data acquired by a temperature sensor. If no matched variable is found, feedback may be given that the necessary data does not exist.
  • FIG. 6 is a schematic diagram of the result 600 of matching between the process procedures of a product IoT model and the processing steps of a data model.
  • the product IoT model By matching the product IoT model against the data model, it can be determined that the four process procedures P 1 , P 2 , P 3 and P 4 of the process C03 of unit No. V101971 match the four processing steps S1, S2, S3 and S4 of the data model, and thus according to the product IoT model, it can be determined that the order No. which the machine tool currently processes in the process C03 is 0400110301.
  • an incorrect data model 700 shown in FIG. 7 is obtained after an analysis of vibration data because each procedure may have a different time interval. It can be seen that the vibration data model in FIG. 7 comprises three steps Sa, Sb and Sc.
  • the results can be manually changed to four steps, and the change is fed back to the data analysis engine to optimize the rules and the parameters adopted during learning.
  • the time interval can be reduced from the order of seconds to the order of microseconds.
  • the data of the vibration sensor of X6140 matches the process C03 of V101971 in order No. 0400110301.
  • the data of the vibration sensor matches the production process, it can be determined that the order No. the machine tool is processing in the process C03 is 0400110301.
  • a method for monitoring the status of a production order can be used to match raw data in factory production against a production procedure to determine the production status in a factory, for example, the status of an order and the status of a device.
  • the cost of marking data is reduced and the production is more efficient.
  • the method can help the manager to learn the production status in the factory and schedule the production.
  • the apparatus and method for monitoring the status of a production order are described by reference to FIGS. 1 to 7 .
  • the apparatus for monitoring the status of a production order can be realized by hardware, software or a combination of hardware and software.
  • FIG. 8 is a block diagram of the electronic device 800 for monitoring the status of a production order according to one embodiment of the disclosure.
  • the electronic device 800 may comprise at least one processor 802 and the processor 802 executes at least one computer-readable instruction (namely, the above-mentioned elements realized in the form of software) stored or coded in a computer-readable storage medium (namely, memory 1004 ).
  • computer-executable instructions are stored in the memory 804 , and when the computer-executable instructions are executed, at least one processor 802 completes the following actions: generating at least one production IoT model on the basis of a production scheduling system document, the . . .
  • production IoT model comprising at least process attributes of product processing; generating at least one product IoT model on the basis of a product design specification document, the product IoT model also comprising at least process attributes of product processing; associating a production IoT model with a product IoT model having the same process attributes; learning data of a production device acquired by a data acquisition automation control system in a factory to obtain a data model representing processing steps of a product; and matching the processing steps against the process attributes of the product IoT model and determining the status of the production order in the factory on the basis of the matching result.
  • At least one processor 802 will execute various operations and functions described in the embodiments of the disclosure in combination with FIGS. 1 to 7 .
  • a non-transitory machine-readable medium can have machine-executable instructions (namely, the above-mentioned elements realized in the form of software).
  • machine-executable instructions namely, the above-mentioned elements realized in the form of software.
  • the machine executes various operations and functions described in the embodiments of the disclosure in combination with FIGS. 1 to 7 .
  • a computer program comprises computer executable instructions, and when the computer executable instructions are executed, at least one processor executes the operations and functions described in the embodiments of the disclosure in combination with FIGS. 1 to 7 .
  • a computer program product comprises computer executable instructions, and when the computer executable instructions are executed, at least one processor executes the operations and functions described in the embodiments of the disclosure in combination with FIGS. 1 to 7 .
  • Next process 308 Previous process 401: Object 402: Process parameter 4021, 4022, 4023, Processing steps 4024: 4021-1, 4022-1, Processing time 4023-1, 4024-1: 4021-2, 4022-2, Rotation speeds 4023-2, 4024-2: 403: Next process 404: Previous process 405: Tool 4051: Machine tool 500: Vibration data model S1, S2, S3, S4: Four processing steps in data model 600: Matching result Pl, P2, P3, P4: Four process procedures 700: Incorrect data model Sa, Sb, Sc: Three processing steps in incorrect data model 800: Electronic device 802: Processor 804: Memory

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